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Sarcoma classification by DNA methylation profiling

by Kommoss, Felix K. F. , Schick, Matthias , Brandner, Sebastian , de Álava, Enrique , Ebrahimi, Azadeh , Esteller, Manel , Heining, Christoph , Koelsche, Christian , Vokuhl, Christian , Beck, Katja , Korshunov, Andrey , Mentzel, Thomas , Cuevas-Bourdier, Adrian , Romero-Pérez, Laura , Hench, Jürgen , Wardelmann, Eva , Hasselblatt, Martin , Sill, Martin , Suwala, Abigail , Weidema, Marije E. , Dirksen, Uta , Kriegsmann, Mark , Unterberg, Andreas , Büttner, Reinhard , Ecker, Jonas , Hartmann, Christian , Benhamida, Jamal K. , von Deimling, Andreas , Jaunmuktane, Zane , Hartmann, Wolfgang , Schirmacher, Peter , Martín, Juan Díaz , Versleijen-Jonkers, Yvonne M. H. , Mora, Jaume , Grünewald, Thomas G. P. , Fernández-Klett, Francisco , Wick, Wolfgang , Staszewski, Ori , Klingebiel, Thomas , Schittenhelm, Jens , Baumhoer, Daniel , del Muro, Xavier Garcia , Horak, Peter , Griewank, Klaus , Mautner, Viktor F. , Fröhling, Stefan , Blattner, Mirjam , Tirado, Oscar M. , Low, Sharon Yin Yee , Bewerunge-Hudler, Melanie , Kool, Marcel , Gastearena, Miguel A. Idoate , Hovestadt, Volker , Brors, Benedikt , Schüller, Ulrich , Wefers, Annika K. , Mynarek, Martin , Wesseling

in 13 / 49 / 49/22 / 49/61 / 631/114 / 631/114/1386 / 631/67/1798 / 706/648/697/129 / Adolescents / Algorithms / Bone cancer / Bone Neoplasms - classification / Bone Neoplasms - diagnosis / Bone Neoplasms - genetics / Bone tumors / Children / Classification / Classifiers / Cohort Studies / Deoxyribonucleic acid / Diagnostic software / Diagnostic systems / DNA / DNA Copy Number Variations - genetics / DNA fingerprinting / DNA Methylation / Humanities and Social Sciences / Humans / Internet / Learning algorithms / Machine Learning / multidisciplinary / Reproducibility of Results / Sarcoma / Sarcoma - classification / Sarcoma - diagnosis / Sarcoma - genetics / Science / Science (multidisciplinary) / Sensitivity and Specificity / Soft Tissue Neoplasms - classification / Soft Tissue Neoplasms - diagnosis / Soft Tissue Neoplasms - genetics / Soft tissues / Tumors

2021

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2021

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Journal Article

Sarcoma classification by DNA methylation profiling

Kommoss, Felix K. F.,

Schick, Matthias,

Brandner, Sebastian,

de Álava, Enrique,

Ebrahimi, Azadeh,

Esteller, Manel,

Heining, Christoph,

Koelsche, Christian,

Vokuhl, Christian,

Beck, Katja,

Korshunov, Andrey,

Mentzel, Thomas,

Cuevas-Bourdier, Adrian,

Romero-Pérez, Laura,

Hench, Jürgen,

Wardelmann, Eva,

Hasselblatt, Martin,

Sill, Martin,

Suwala, Abigail,

Weidema, Marije E.,

Dirksen, Uta,

Kriegsmann, Mark,

Unterberg, Andreas,

Büttner, Reinhard,

Ecker, Jonas,

Hartmann, Christian,

Benhamida, Jamal K.,

von Deimling, Andreas,

Jaunmuktane, Zane,

Hartmann, Wolfgang,

Schirmacher, Peter,

Martín, Juan Díaz,

Versleijen-Jonkers, Yvonne M. H.,

Mora, Jaume,

Grünewald, Thomas G. P.,

Fernández-Klett, Francisco,

Wick, Wolfgang,

Staszewski, Ori,

Klingebiel, Thomas,

Schittenhelm, Jens,

Baumhoer, Daniel,

del Muro, Xavier Garcia,

Horak, Peter,

Griewank, Klaus,

Mautner, Viktor F.,

Fröhling, Stefan,

Blattner, Mirjam,

Tirado, Oscar M.,

Low, Sharon Yin Yee,

Bewerunge-Hudler, Melanie,

Kool, Marcel,

Gastearena, Miguel A. Idoate,

Hovestadt, Volker,

Brors, Benedikt,

Schüller, Ulrich,

Wefers, Annika K.,

Mynarek, Martin,

Wesseling

2021

Overview

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications. Sarcomas are morphologically heterogeneous tumours rendering their classification challenging. Here the authors developed a classifier using DNA methylation data from several soft tissue and bone sarcoma subtypes, which has the potential to improve classification for research and clinical purposes.

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Publisher

Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio

Subject

/ 49

/ 49/22

/ 49/61

/ 631/114

/ 631/114/1386

/ 631/67/1798

/ 706/648/697/129